Modeling the Impact of Thread Configuration on Power and Performance of GPUs
Student: Tiffany A. Connors (Texas State University)
Supervisor: Apan Qasem (Texas State University)
Abstract: Because graphics processing units (GPUs) are a low-cost option for achieving high computational power, they have become widely used in high-performance computing. However, GPUs can consume large amounts of power. Due to the associated energy costs, improving energy-efficiency has become a growing concern. By evaluating the impact of thread configuration on performance and power trade-off, energy-efficient solutions can be identified.
The impact that a thread configuration will have on the performance and power trade-off of a GPU kernel can be accurately predicted using machine learning. Using dynamic features of a GPU kernel as input, a machine learning model can be used to assist in the selection of thread configurations which will improve performance and minimize power consumption.
Two-page extended abstract: pdf